Driver-Monitoring System

ABSTRACT

The techniques of this disclosure relate to a driver-monitoring system. The system includes a controller circuit that receives monitoring data from a driver-monitor sensor that is configured to monitor a driver of a vehicle while the vehicle operates in an autonomous-driving mode. The controller circuit determines a score of one or more driver-supervisory metrics based on the monitoring data, each of the driver-supervisory metrics being indicative of whether the driver is supervising the operation of the vehicle. The controller circuit determines a supervision score based on the score of the driver-supervisory metrics. The supervision score is indicative of whether the driver is ready to resume control of the vehicle. The controller circuit indicates a driver-awareness status on a display in a field of view of the driver based on the supervision score. The system can improve vehicle safety by alerting the driver when the driver exhibits reduced driver awareness behavior.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. 119(e) of U.S.Provisional Application No. 62/957,426 filed Jan. 6, 2020, thedisclosure of which is hereby incorporated by reference in its entiretyherein.

BACKGROUND

A Society of Automotive Engineers (SAE) Level 2 automated driving systemincludes driver-assistance features that provide steering, braking, andacceleration assistance, for example, lane centering and adaptive cruisecontrol. Vehicles equipped with Level 2 automated-driving systemsrequire a human driver to be poised to take control of the vehicle inthe event the automated-driving system relinquishes control. While thevehicle is operating under Level 2 automated control, the driver isresponsible for supervising the automated-driving features and theenvironment surrounding the vehicle. When an inattentive driver isrequired to take control back from the automated-driving system, thedriver may not react in time to avoid an accident or collision. Someautomated-driving systems issue warnings to the inattentive driver whenthe driver removes their hands from a steering wheel or is looking awayfrom the roadway for a predefined period of time. In some cases, driverscan overcome these monitoring systems by placing an object on thesteering wheel to simulate a torque input from the driver's hands orplace a photograph in a driver's seat to give an appearance of thedriver attentively watching the roadway.

SUMMARY

This document describes one or more aspects of a driver-monitoringsystem. In one example, the system includes a controller circuitconfigured to receive monitoring data from a driver-monitor sensor thatis configured to monitor a driver of a vehicle while the vehicleoperates in an autonomous-driving mode. The controller circuit is alsoconfigured to determine a score of one or more driver-supervisorymetrics based on the monitoring data, each of the driver-supervisorymetrics being at least partially indicative of whether the driver issupervising the operation of the vehicle. The controller circuit is alsoconfigured to determine a supervision score based on the score of theone or more driver-supervisory metrics. The supervision score isindicative of whether the driver is ready to resume control of thevehicle. The controller circuit is also configured to indicate adriver-awareness status on a display in a field of view of the driverbased on the supervision score.

In another example, a method includes receiving, with a controllercircuit, monitoring data from a driver-monitor sensor configured tomonitor a driver of a vehicle while the vehicle operates in anautonomous-driving mode. The method also includes determining, with thecontroller circuit, a score of one or more driver-supervisory metricsbased on the monitoring data. The method also includes determining, withthe controller circuit, a supervision score based on the score of theone or more driver-supervisory metrics. The method also includesindicating, with the controller circuit, a driver-awareness status on adisplay in a field of view of the driver based on the supervision score.

This summary is provided to introduce aspects of a driver-monitoringsystem, which is further described below in the Detailed Description andDrawings. For ease of description, the disclosure focuses onvehicle-based or automotive-based systems, such as those that areintegrated on vehicles traveling on a roadway. However, the techniquesand systems described herein are not limited to vehicle or automotivecontexts but also apply to other environments where cameras can be usedto detect objects. This summary is not intended to identify essentialfeatures of the claimed subject matter, nor is it intended for use indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more aspects of a driver-monitoring system aredescribed in this document with reference to the following drawings. Thesame numbers are used throughout the drawings to reference like featuresand components:

FIG. 1 illustrates an example of a driver-monitoring system mounted on avehicle;

FIG. 2 illustrates an example driver-monitor sensor isolated from theexample of a driver-monitoring system of FIG. 1;

FIG. 3 illustrates an example of driver-supervisory metrics of thedriver-monitoring system of FIG. 1;

FIG. 4 illustrates an example of an attentional buffer value plot of asituational awareness driver-supervisory metric;

FIG. 5 illustrates an example of a supervision score based on thedriver-supervision metric scores;

FIG. 6 illustrates another example of the supervision score based on thedriver-supervision metric scores;

FIG. 7 illustrates yet another example of the supervision score based onthe driver-supervision metric scores;

FIG. 8 illustrates an example of a display of the driver-monitoringsystem of FIG. 1 extending along a dashboard of the vehicle;

FIG. 9 illustrates an example of the supervision scores and associatedcolors that can be illuminated on the display of FIG. 8;

FIG. 10 illustrates an example of the display of the driver-monitoringsystem of FIG. 1 extending along a door of the vehicle;

FIG. 11 is an example flow chart of an example logic flow performed by acontroller circuit of the system of FIG. 1; and

FIG. 12 is an example method of operating the example driver-monitoringsystem of FIG. 1.

DETAILED DESCRIPTION Overview

The techniques of this disclosure relate to a driver-monitoring system.A controller circuit receives data from in-cabin sensors that detectwhether a driver is supervising an automated vehicle operating in anautomated-driving mode. The in-cabin sensors detect whether the driveris properly seated in the driver's seat and whether the driver is payingattention to the vehicle surroundings. The system determines a score ofseveral driver-supervisory metrics that indicate whether the driver issupervising the operation of the vehicle. The system determines asupervision score based on the scores of the driver-supervisory metricsindicative of whether the driver is ready to resume control of thevehicle. The system indicates a driver-awareness status on a lighteddisplay based on the supervision score. The lighted display is locatedin the driver's field of view, and colors illuminated on the displaycorrespond to the driver-awareness status. The controller circuit alertsthe driver when the driver is determined to be inattentive by changingthe colors of the display and pulsating the light on the display. Thecontroller circuit increases a frequency of the pulsations when thedriver's inattentiveness continues over time. The driver-monitoringsystem can improve vehicle safety by alerting the driver to inattentivebehavior when the vehicle is operating in an autonomous-driving mode andenables a smooth handover of control to the driver resulting in animproved user experience.

Example System

FIG. 1 illustrates an example of a driver-monitoring system 100,hereafter referred to as the system 100. The system 100 includes acontroller circuit 102 configured to receive monitoring data 104 from adriver-monitor sensor 106 installed on a vehicle 108. The driver-monitorsensor 106 can be a component of an occupant classification system 110(OCS 110) installed on the vehicle 108, which will be explained in moredetail below. The controller circuit 102 is configured to determine asupervision score 112 (see FIG. 5) of the driver and indicate adriver-awareness status 114 on a display 116 that is visible in a fieldof view of the driver.

Although the vehicle 108 can be any vehicle, for ease of description,the vehicle 108 is primarily described as being a self-drivingautomobile that is configured to operate in an autonomous-driving modeto assist the driver riding onboard the vehicle 108. The vehicle 108 canbe capable of a Society of Automotive Engineers (SAE) Level 2 autonomousoperation that assists the driver with steering, braking, andacceleration while the driver monitors the operation of the vehicle 108at all times from a driver's seat.

In the example illustrated in FIG. 1, the controller circuit 102 isinstalled on the vehicle 108 and is communicatively coupled to thedriver-monitor sensor 106 and the display 116 via transmission links.The transmission links can be wired or wireless interfaces, for example,BLUETOOTH®, Wi-Fi®, near field communication (NFC), universal serial bus(USB), universal asynchronous receiver/transmitter (UART), or controllerarea network (CAN). In some examples, the controller circuit 102receives data from other vehicle systems via a CAN bus (not shown), forexample, an ignition status, a vehicle speed, a vehicle relative motion,and a transmission gear selection.

Controller Circuit

The controller circuit 102 may be implemented as a microprocessor orother control circuitry such as analog and/or digital control circuitry.The control circuitry may include one or more application-specificintegrated circuits (ASICs), field-programmable gate arrays (FPGAs) thatare programmed to perform the techniques, or one or more general-purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. The controller circuit 102 may also combine customhard-wired logic, ASICs, or FPGAs with custom programming to perform thetechniques. The controller circuit 102 may include a memory or storagemedia (not shown), including non-volatile memory, such as electricallyerasable programmable read-only memory (EEPROM) for storing one or moreroutines, thresholds, and captured data. The EEPROM stores data andallows individual bytes to be erased and reprogrammed by applyingprogramming signals. The controller circuit 102 may include otherexamples of non-volatile memory, such as flash memory, read-only memory(ROM), programmable read-only memory (PROM), and erasable programmableread-only memory (EPROM). The controller circuit 102 may includevolatile memory (e.g., dynamic random-access memory (DRAM), staticrandom-access memory (SRAM)). The controller circuit 102 can include oneor more clocks or timers used to synchronize the control circuitry ordetermine an elapsed time of events. The one or more routines may beexecuted by the processor to perform steps for determining thesupervision score 112 based on signals received by the controllercircuit 102 from the driver-monitor sensor 106 as described herein.

Driver-Monitor Sensor

FIG. 2 illustrates an example of the driver-monitor sensor 106 that islocated remotely from the system 100. The driver-monitor sensor 106 isconfigured to monitor the driver of the vehicle 108, as will bedescribed in more detail below. The driver-monitor sensor 106 caninclude one or more sensors that detect aspects of the driver and can becomponents of the OCS 110 installed on the vehicle 108. Thedriver-monitor sensor 106 can include a camera that captures images ofthe driver, and the OCS 110 determines whether the driver's seat isoccupied by a person based on the images. Software executed by the OCS110 can distinguish persons from animals and objects using knownimage-analysis techniques. The objects in the images are detected inregions of interest that correspond to the seating positions (e.g., thedriver's seat position) within the cabin, and the objects are classifiedby the software into human and other classifications. Processing blocksor models in the software are pre-trained to recognize human forms orshapes of other objects, for example, a shopping bag, a box, or ananimal. The camera can be a two-dimensional (2D) camera or a 3Dtime-of-flight camera that measures a time for light pulses to leave thecamera and reflect back on the camera's imaging array.

The OCS 110 can use the images captured by the camera to determinewhether the driver is correctly positioned in the driver's seat. The OCS110 can use known localization techniques to localize a head or upperbody of the driver and determine whether the driver is positioned withina zone of the driver's seat. The OCS 110 can include head and bodymodels with known rotations and dynamics within the driver's seat thatcorrespond to normal driving behaviors. The OCS 110 can compare theposition of the head and upper body in the images to the head and bodymodels to determine whether the driver is correctly positioned in thedriver's seat.

The OCS 110 can use the images captured by the camera to determine thedriver's gaze direction, for example, whether the driver is looking at aroadway ahead of the vehicle 108 or looking in other directions. The OCS110 can use known eye-tracking techniques to determine the gazedirection based on a localization of a pupil of the eyes and adetermined head pose. The OCS 110 can keep track of the time that thedriver's gaze is fixed in a particular direction (e.g., fixation) andcan count a frequency of quick movements of the eyes between differentfixated positions (e.g., saccades). The OCS 110 can use the driver'sgaze information to indicate a situational-awareness level of thedriver, as will be explained in more detail below. In some examples, aninfrared (IR) light source is used to illuminate the eyes to enable thelocalization of the pupils when the driver is wearing glasses or undernighttime conditions.

The OCS 110 can use the images captured by the camera to track theamount of time that the driver's eyes are closed to determine adrowsiness level and a sleep level. The OCS 110 can use eye models todetermine whether eyelids are occluding irises of the eyes based on theimages captured by the camera. The OCS 110 can determine an amount ofopenness of the eyelids and a time duration that the eyes are closed asan indication of drowsiness or sleep.

The driver-monitor sensor 106 can also include a seat-pressure sensorthat detects a pressure or pressure distribution applied to the seat.The OCS 110 can determine whether the driver is occupying the driver'sseat based on a pressure threshold indicative of a weight of the driver.For example, if the weight of the occupant is greater than thirtykilograms, the OCS 110 may determine that the driver is considered anadult. The pressure distribution can indicate whether the objectoccupying the driver's seat is a person or an object other than aperson. The pressure distribution can also indicate whether the driveris in the correct position within the driver's seat; for example, whenthe driver is leaning over to one side of the seat, the pressure isconcentrated on one side of the seat.

The driver-monitor sensor 106 can also include a steering-wheel-torquesensor that detects a torque applied to the steering wheel. The torquecan be detected when the driver places a hand on the steering wheel evenwith the autonomous control system steering the vehicle 108. Thesteering-wheel-torque sensor can be an electro-mechanical deviceintegrated into a power steering system of the vehicle 108 thatdetermines a torsion bar angle required for the steering movement. Thesteering-wheel-torque sensor can also output a steering angle and rateof change of the steering wheel angular position.

The driver-monitor sensor 106 can also include a capacitivesteering-wheel sensor that detects a touch of the driver's hands on thesteering wheel. The capacitive steering-wheel sensors can be located ina rim of the steering wheel and can detect contact points of the handswith the steering wheel. In some examples, touching the steering wheelwith the hands distorts an electric field generated by the sensor andchanges a capacitance of the sensor, indicating the presence of thedriver's hand. The capacitive steering-wheel sensor can detect whetherone or both driver's hands are on the steering wheel.

The driver-monitor sensor 106 can also include a radar sensor thatdetects a presence of objects in the vehicle cabin, and the OCS 110 candetermine whether the driver's seat is occupied by the driver or theobject based on point cloud data received from the radar sensor. The OCS110 compares the point cloud data to models in the software to determinewhether the seat is occupied by the person or the object. In someexamples, the radar sensor can detect relatively small movements, forexample, movements of a chest wall of the driver that is breathing.

The monitoring data 104 from the OCS 110 can be periodically updated bythe OCS 110 to ensure the controller circuit 102 can accuratelydetermine the supervision score 112. For example, the OCS 110 can updatethe monitoring data 104 at one-second intervals to account for temporarychanges in the seat occupancy or attentiveness of the driver.

The OCS 110 can use machine learning to detect the various driveraspects and behaviors. Machine learning is a data analytics techniquethat teaches computers to learn from experience. Machine learningroutines, or algorithms, use computational methods to learn informationfrom data without relying on a predetermined equation as a model. Theroutines improve their performance as the sample size available forlearning increases. Machine learning uses two types of techniques:supervised learning, which trains a model on known input and output dataso that it can predict future outputs, and unsupervised learning, whichfinds hidden patterns or intrinsic structures in input data. Supervisedlearning uses classification and regression techniques to developpredictive models. Common algorithms for performing classificationinclude support vector machine (SVM), boosted and bagged decision trees,k-nearest neighbor, Naïve Bayes, discriminant analysis, logisticregression, and neural networks. Common regression algorithms includelinear model, nonlinear model, regularization, stepwise regression,boosted and bagged decision trees, neural networks, and adaptiveneuro-fuzzy learning. Unsupervised learning finds hidden patterns orintrinsic structures in data and is used to draw inferences fromdatasets consisting of input data without labeled responses. Clusteringis a common unsupervised learning technique. Common algorithms forperforming clustering include k-means and k-medoids, hierarchicalclustering, Gaussian mixture models, hidden Markov models,self-organizing maps, fuzzy c-means clustering, and subtractiveclustering. In the context of self-driving automobiles, the OCS 110 canuse machine learning specifically to detect based on the driver-monitorsensor 106 an attentiveness of the driver or other aspects of drivingbehavior that feed the driver-supervisory metrics 118 to ensure thecontroller circuit 102 can accurately determine the supervision score112.

Driver-Supervisory Metrics

FIG. 3 is a table illustrating an example of driver-supervisory metrics118 that are indicative of whether the driver is supervising theoperation of the vehicle. FIG. 3 is not intended to be an exhaustivelist of the driver-supervisory metrics 118, as other metrics may beincluded as defined by the user. The controller circuit 102 candetermine a score of driver-supervisory metrics 118 based on themonitoring data 104 received from the OCS 110. The scores of thedriver-supervisory metrics 118 can be user-defined, and in the exampleillustrated in FIG. 3, range from zero to 100, with zero indicating ahighest score and 100 indicating a lowest score for the respectivemetrics. For example, the score of zero for the hands-off-steering-wheeltime indicates that the driver's hands are in contact with the steeringwheel for most of the time, and the score of 100 indicates that thedriver's hands are off the steering wheel for a time greater than athreshold, as will be explained below. Some of the driver-supervisorymetrics 118 are binary (e.g., either zero or 100), for example, thedriver sleep-state where zero indicates the driver is awake and 100indicates the driver is asleep.

The driver-supervisory metrics 118 include the situational awareness ofthe driver that is based in part on a driver's glance direction, adriver's glance duration, and a glance count of the driver, as detectedby the camera and determined by the OCS 110. The score of thesituational-awareness metric is calculated based on a buffer algorithmdeveloped by the Advanced Human Factors Evaluator for Automotive Demand(AHEAD) Consortium, sponsored by the Massachusetts Institute ofTechnology (MIT) of Cambridge, Mass., USA, and including members fromthe automotive and insurance industries and consumer advocacy entities.The buffer algorithm uses an attentional buffer design where the bufferis initially full (e.g., a value of 2.0) and decreases to zero as thedriver makes off-road glances. The situational awareness is calculatedusing the equation (2-Buffer)*50, where the buffer value of 2 indicatesa high situational awareness resulting in the driver-supervisory metricscore of zero, the buffer value of 1 indicates a cognitive overloadresulting in the driver-supervisory metric score of 50, and the buffervalue of zero indicates low situational awareness resulting in thedriver-supervisory metric score of 100. The buffer algorithm alsoconsiders a gaze fixation or vision tunneling, for example, when thedriver is looking in a same direction for an extended time. Gazefixation may be correlated with cognitive workload or mentaldistraction, and although the driver appears to be looking at theroadway, the attentional buffer may decrease because the driver is notscanning the surroundings for developing traffic situations.

FIG. 4 illustrates an example of the attentional buffer changing overtime as the driver glances in different directions for different periodsof time, as determined by the OCS 110. The buffer starts at a value of 2at 10 seconds and decreases to the value of 1 when the driver glancesaway from a forward direction between 9 seconds and 8 seconds. Thedriver glances back to forward between 8 seconds and 7 seconds, wherethe buffer increases to about 1.25. The driver continues to glance awayfrom forward for longer periods of time, and at 5 seconds, the buffervalue is zero indicating the low situational-awareness level. From about4.5 seconds to 3 seconds, the driver's gaze returns to the forwarddirection, and the buffer increases back to the maximum value of 2. Thebuffer algorithm does not penalize the driver for glancing in directionsthat relate to the surroundings or pertain to the operation of thevehicle 108, as illustrated in FIG. 4 at about 2 seconds, where thedriver glances at the vehicle mirror or speedometer and the buffermaintains the maximum value of 2.

Referring back to FIG. 3, the driver-supervisory metrics 118 include adistraction level of the driver that is scored from zero, when thedriver is considered not to be distracted, up to 100 when the driver isconsidered to be distracted. The distraction level can be determined bythe controller circuit 102 based on the time that the driver isdetermined to be looking at the roadway. For example, the OCS 110 candefine an on-road area of interest that corresponds to the driver's sideof the windshield. When the OCS 110 determines that the driver is notlooking at the on-road area of interest for a period of time, the OCS110 can determine that the driver is distracted. The controller circuit102 can use the equation MIN(100,100*(EYES-OFF-ROAD-TIME/2000)) todetermine the score between zero and 100, where the eyes-off-road timeis the time in milliseconds (ms) that the driver is not looking towardthe on-road area of interest. The controller circuit 102 can monitor thedriver's gaze direction for a maximum duration of 2000 ms (i.e., 2seconds) whereby the score becomes the maximum value of 100. Thedistraction level differs from the situational awareness in that it doesnot consider the glance behavior, only whether the driver is looking ina forward direction toward the windshield.

The driver-supervisory metrics 118 include an object-in-hand detectionof the driver that can be detected by the camera and determined by theOCS 110 using known object recognition and classification techniques.For example, the camera can capture the image of the driver holding amobile phone or a book, and the OCS 110 can determine the identity ofthe object being held in the driver's hands. The controller circuit 102can include a library of images of known objects to compare to theobject detected in the driver's hand and determine whether to assign thescore of zero or 100. For example, the mobile phone or the book may begiven a score of 100, while a bottle of water that the driver lifts todrink from may be assigned the score of zero.

The driver-supervisory metrics 118 can include ahands-off-steering-wheel time of the driver that can be determined bythe controller circuit 102 using timers in conjunction with the imagesfrom the camera or the capacitive steering-wheel sensor. Thehands-off-steering-wheel times can be scored from zero to 100 bymultiplying the time in seconds that the hands are determined to be offthe steering wheel by a factor of 10. For example, a value of zero whenthe hands are detected off the steering wheel for a time less than athreshold (e.g., less than 1 second), a value of 50 when the hands aredetermined to be off the steering wheel for a time exceeding anotherthreshold (e.g., greater than 5 seconds), and a value of 100 when thehands are determined to be off the steering wheel for a time exceedingyet another threshold (e.g., greater than 10 seconds). The OCS 110 candetermine whether the driver removes their hands from the steeringwheel, and the controller circuit 102 can start the timer until the OCS110 determines the driver's hands have returned to the steering wheel.

The driver-supervisory metrics 118 can include a driver presence. Thedriver presence can be determined by the OCS 110 using the camera or theseat-pressure sensor, as described above. The driver presence metric isscored as the binary value of either zero when the driver is present inthe driver's seat or 100 when the driver is determined not to be presentin the driver's seat.

The driver-supervisory metrics 118 can include a driver-body position ofthe driver. The driver-body position can be determined by the OCS 110using the camera or the seat-pressure sensor, as described above. Thedriver-body position metric is scored as the binary value of eitherzero, when the driver is in position in the driver's seat, or 100, whenthe driver is determined not to be in position in the driver's seat.

The driver-supervisory metrics 118 can include a driver sleep-state. Thedriver sleep-state is scored as the binary value of either zero when thedriver is awake or 100 when the driver is determined to be asleep. Thedriver sleep-state can include microsleep events that are temporaryperiods of sleep that can last from a few microseconds to tens ofseconds. Microsleep events can manifest as droopy eyelids, slow eyelidclosure, and head nodding. The OCS 110 can detect that the driver isexhibiting microsleep events by monitoring the eyes and the head pose ofthe driver using the camera as described above. The controller circuit102 can track the time of the eyelid closures and determine whether theeyelid closures last for a time significantly longer than an eye blinkthat can indicate sleep or microsleep events.

The driver-supervisory metrics 118 can include a driver-drowsinesslevel. The driver-drowsiness level is related to the driver sleep-statein that the driver-drowsiness level is part of a continuum of sleep. Forexample, when the driver sleep-state is zero (e.g., fully awake), thedriver is not indicating signs of drowsiness, and when the driversleep-state is 100 (e.g., asleep), the driver has transitioned fromdrowsy to asleep. As such, the scores of the driver-drowsiness levelillustrated in FIG. 3 do not include zero and 100. The OCS 110 candetermine the driver-drowsiness level by monitoring the driver's eyesand head pose, as described above. The scores of driver-drowsinessincreases as the counts of occurrences of eyelid closures and head nodsincrease over a given period. For example, the OCS 110 can determine apercentage of eyelid closure over the pupil (PERCLOS) over time thatdifferentiates slow eyelid closures or droops from eye blinks. As thePERCLOS increases, the driver-drowsiness level score can be scaled from10 to 90 to correspond with the percentage of time the eyes are occludedover the sampled period of time, as illustrated in FIG. 3.

Supervision Score

FIG. 5. illustrates an example of the supervision score 112 determinedby the controller circuit 102 that is based on the scores of thedriver-supervisory metrics 118. The supervision score 112 is indicativeof whether the driver is ready to resume control of the vehicle 108 ifthe vehicle system exits autonomous-driving mode or in response to adeveloping traffic situation that the vehicle system may not anticipate.The controller circuit 102 can determine the supervision score 112 bydetermining a maximum value of any one of the driver-supervisory metrics118. In the example illustrated in FIG. 5, the driver is determined tohave the high situational awareness with the score of zero, thehands-off-steering-wheel time less than the first threshold (e.g., lessthan one second) with the score of zero, the driver is present and inthe correct position in the driver's seat with the score of zero, andthe driver indicates a high alertness level with the score of zero forthe driver sleep-state. The controller circuit 102 selects thedriver-supervisory metric 118 with the maximum score, which in thisexample is zero.

FIG. 6. illustrates another example of the supervision score 112determined by the controller circuit 102, where the driver is determinedto exhibit a cognitive overload with the score of 50 for the situationalawareness, as determined by the buffer algorithm described above. Inthis example, the buffer value is 1, generating the score of(2−1)*50=50. The OCS 110 determines that the hands-off-steering-wheeltime greater than 5 seconds with a score of 50, and determines that thedriver has a driver-drowsiness level score of 20 based on the PERCLOS,as described above. The scores of the remaining driver-supervisorymetrics 118 are zero. In this example, the controller circuit 102selects the driver-supervisory metric 118 with the maximum score, whichis 50.

FIG. 7. illustrates another example of the supervision score 112determined by the controller circuit 102, where the driver is determinedto exhibit low situational awareness with the score of 100, asdetermined by the buffer algorithm described above. In this example, thebuffer value is zero generating the score of (2−0)*50=100. The OCS 110determines that the hands-off-steering-wheel time greater than 10seconds with a score of 100, and determines that the driver is not inthe driver's seat or out of position, each with scores of 100. Thescores of the remaining driver-supervisory metrics 118 are zero. In thisexample, the controller circuit 102 selects the driver-supervisorymetric 118 with the maximum score, which is 100.

The controller circuit 102 can use other techniques to determine thesupervision score 112 besides selecting the maximum value of thedriver-supervisory metrics 118. For example, the controller circuit 102can determine an average value of all the driver-supervisory metrics 118and determine the supervision score 112 based on the average value orcan determine a maximum value of the driver-supervisory metrics 118 overa period of time to reduce a variation in the supervision score 112.

The controller circuit 102 can indicate the driver-awareness status 114based on the supervision score 112 on the display 116 in the field ofview of the driver, as described below.

Display

FIG. 8 illustrates an example of the display 116 that is a light barextending along a length of a dashboard located just below thewindshield of the vehicle 108. The controller circuit 102 can change acolor of light illuminated on the light bar based on the supervisionscore 112 to indicate the driver-awareness status 114 to the driver, aswill be described in more detail below. The light bar can includedifferent light-emitting diodes (LEDs) configured to illuminatedifferent colors of light based on signals received from the controllercircuit 102. In the example illustrated in FIG. 8, the controllercircuit 102 indicates the driver-awareness status 114 by illuminating agreen light on the light bar that corresponds to the supervision score112 of zero. As the supervision score 112 increases from zero to 100,the controller circuit 102 can indicate the driver-awareness status 114by changing the color of light on the light bar from green to red,including additional colors that indicate scores between zero and 100.

FIG. 9 illustrates an example of the supervision scores 112 andassociated driver-awareness status 114 ranging from green with awavelength (A) of 550 nanometers (nm) to red with the wavelength of 700nm. Other colors of the visible light spectrum between green and red areindicated by their respective wavelengths. For example, the light havingthe wavelength of 580 nm is yellow, and the light having the wavelengthof 620 nm is orange. The light colors can be predetermined by thevehicle manufacturer or can be selected by the vehicle operator based onthe operator's preferences. The driver-awareness status 114 of greenindicates that the driver is ready to take control of the vehicle onshort notice, and the driver-awareness status 114 of red indicates aserious vehicle safety concern where the driver may not be able to reactin time to avoid an accident, should the autonomous-driving mode fail torespond to a threat.

The controller circuit 102 can pulsate the light displayed by the lightbar when the supervision score 112 exceeds a first threshold, asindicated in the “EFFECT” column of FIG. 9. The controller circuit 102can pulsate the light at a first frequency, for example, at two-secondintervals or one-half Hertz (0.5 Hz). The first threshold can beuser-defined, and in the example illustrated in FIG. 9, the firstthreshold is the supervision score 112 of 80, where the driver'ssupervision score 112 indicates that the driver is exhibiting lowattentiveness. The controller circuit can increase the frequency oflight pulsations when the supervision score exceeds the first thresholdfor a time exceeding a second threshold. For example, when thesupervision score 112 exceeds 80 for a period of 20 seconds, thecontroller circuit 102 can increase the pulsation frequency of the lightfrom 0.5 Hz to 1 Hz.

FIG. 10 illustrates an example of the light bar extending along thelength of a door of the vehicle 108. The light bar can extend along thelength of both a left-side and a right-side door of the vehicle 108 thatmay be in the driver's field of view when the driver is looking to theleft or right side of the vehicle 108. The controller circuit 102 canilluminate the door-mounted light bars with the same colors and effectsas the light bar located on the dashboard, as described above. Thecontroller circuit 102 can use the monitoring data 104 from the OCS 110to determine that the gaze direction of the driver is directed towardthe left-side or right-side doors and chase 120 the light pulsations onthe light bar based on the gaze direction, thereby directing a driver'sgaze toward the front of the vehicle 108, as illustrated in FIG. 10. Thechase 120 is an electrical application where the adjacent LEDs arecycled on and off to give an illusion that the lights are moving in aparticular direction along the light bar. The controller circuit 102 canuse different chase patterns to direct the driver's gaze toward thefront of the vehicle 108, for example, illuminating progressively largerand larger clusters of LEDs as the chase 120 moves down the light bar orilluminating large sections of LEDs as the chase 120 moves down thelight bar.

FIG. 11 is a flow diagram illustrating an example logic flow 200performed by the controller circuit 102. The logic flow starts at 202with indicating the driver-awareness status 114 on the display 116 uponvehicle ignition and ends at 214 with increasing the pulsationfrequency. In this example, at 202, upon the driver actuating a vehicleignition switch inside the vehicle 108, the controller circuit 102determines the supervision score 112 based on the scores of thedriver-supervisory metrics 118, as described above. The controllercircuit 102 indicates the driver-awareness status 114 on the light bar,as illustrated in FIGS. 8-10, and at 204, determines whether thesupervision score 112 is greater than a threshold. If the supervisionscore 112 is greater than the threshold (e.g., greater than 80), at 206,the controller circuit 102 starts the timer.

At 208, the controller circuit 102 begins pulsating the light on thelight bar at the first frequency of 0.5 Hz to alert the driver to thedegraded driver-attentiveness. At 210, the controller circuit 102determines whether the supervision score 112 remains greater than thethreshold. If the supervision score 112 drops below the threshold, thecontroller circuit 102 indicates the new driver-awareness status 114corresponding to the new supervision score 112 on the light bar. If thesupervision score 112 remains above the threshold, at 212, thecontroller circuit 102 determines whether the time is greater than thetime threshold (e.g., 20 seconds). When the time exceeds the timethreshold of 20 seconds, at 214, the controller circuit 102 increasesthe frequency of the pulsations to 1 Hz to further alert the driver tothe degrading driver-attentiveness.

The controller circuit 102 can use other alert methods in addition tothe light color and effects, for example, voice alerts and haptic orvibrational alerts. For example, the voice alerts can call out to thedriver to alert the driver to improve their attentiveness. The hapticalerts can be applied to the driver's seat to remind the driver of theirsupervision responsibilities. These other notification methods can alsobe escalated by increasing the intensity, for example, by increasing avolume of the voice notification, increasing the frequency of therepeated voice notification, and vibrating the driver's seat withincreasing frequency or chasing the seat vibrations toward the front ofthe vehicle.

Example Method

FIG. 11 illustrates example methods 300 performed by the system 100. Forexample, the controller circuit 102 configures the system 100 to performoperations 302 through 308 by executing instructions associated with thecontroller circuit 102. The operations (or steps) 302 through 308 areperformed but not necessarily limited to the order or combinations inwhich the operations are shown herein. Further, any of one or more ofthe operations may be repeated, combined, or reorganized to provideother operations.

Step 302 includes RECEIVE MONITORING DATA. This can include receiving,with the controller circuit 102, monitoring data 104 from thedriver-monitor sensor 106 installed on the vehicle 108 via thetransmission link. The driver-monitor sensor 106 can include multiplesensors that detect aspects or behaviors of the driver and can becomponents of the OCS 110, as described above. The driver-monitor sensor106 can include 2D cameras and 3D cameras that capture video images ofthe driver, and the OCS 110 determines whether the seat is occupied bythe driver or the object based on the images, as described above. Thedriver-monitor sensor 106 can also include the radar sensor that detectsthe presence of the driver in the driver's seat. The OCS 110 can use thecameras to detect the driver's body and head positions and detect thegaze direction of the driver's eyes, as described above. Thedriver-monitor sensor 106 can also include seat-pressure sensors thatdetect when the driver is occupying the seat and can include thesteering-torque and capacitive steering-wheel sensors to detect whetherthe driver's hands are on the steering wheel. The monitoring data 104can be periodically updated by the OCS 110 and transferred to thecontroller circuit 102 to ensure the controller circuit 102 canaccurately determine the supervision score 112.

Step 304 includes DETERMINE DRIVER-SUPERVISORY METRIC SCORES. This caninclude determining, with the controller circuit 102, scores of thedriver-supervisory metrics based on the monitoring data 104 receivedfrom the OCS 110. The driver-supervisory metrics include the situationalawareness, the distraction level, the object-in-hand detection, thehands-off-steering-wheel time, the driver presence in the driver's seat,the driver-body position in the driver's seat, the driver sleep-state,and the driver-drowsiness level, as illustrated in FIG. 3 and describedabove.

The controller circuit 102 determines the situational-awareness scorebased on the driver's glance behavior detected by the camera, asdescribed above. The situational-awareness scores range from zero forhigh situational awareness to 100 for low situational awareness and arebased on the attentional buffer, as described above. The controllercircuit 102 determines the distraction level based on the cameradetecting the gaze direction of the driver toward the on-road area ofinterest, as described above. The distraction level scores are zero forthe undistracted driver and increase up to 100 for the driver that islooking away from the area of interest for more than 2 seconds, asdescribed above.

The controller circuit 102 determines the object-in-hand detection basedon the camera detecting whether the driver is holding a mobile phone orother object that may be an object of the driver's attention. The scoresfor the object-in-hand are zero for no object-in-hand and 100 when theobject is detected in the hand. The controller circuit 102 determinesthe hands-off-steering-wheel time based on the timer and the steeringtorque and capacitive steering-wheel sensors. The scores for thehands-off-steering-wheel time range from zero to 100 and are determinedby multiplying the time in seconds by 10.

The controller circuit 102 determines whether the driver is present inthe driver's seat and whether the driver is positioned correctly in thedriver's seat based on the monitoring data 104 from the cameras, radarsensors, and the seat-pressure sensors. The scores for the driverpresence are zero for the driver being detected in the driver's seat and100 for the driver not being detected. The scores for the driver-bodyposition are zero for the driver being in the correct position and 100for the driver being detected out of position.

The controller circuit 102 determines whether the driver is awake orasleep based on the cameras that detect the eyes and the head pose ofthe driver, and the amount of time eyelids are closed, as describedabove. The scores for the driver sleep-state are zero for fully awakeand 100 for asleep. The controller circuit 102 determines thedriver-drowsiness level based on the driver's eyes and head pose and thePERCLOS as described above. The scores for the driver-drowsiness levelrange from 10 to 90 and reflect the percentage of time the eyes areoccluded, as described above.

Step 306 includes DETERMINE SUPERVISION SCORE. This can includedetermining, with the controller circuit 102, the supervision score 112based on the scores of the driver-supervisory metrics 118, as describedabove. The controller circuit 102 can determine the supervision score112 based on a maximum value of the driver-supervisory metrics 118 or anaverage value of the driver-supervisory metrics 118, as described above.

Step 308 includes INDICATE DRIVER-AWARENESS STATUS. This can includeindicating, with the controller circuit 102, the driver-awareness status114 on the light bar, as described above. The driver-awareness status114 is based on the supervision score 112 and can be displayed byilluminating the light bar with different colors. The light bar can belocated in the driver's field of view, for example, extending along thedashboard and doors of the vehicle 108. The controller circuit 102 canchange the light color and illumination effects to alert the driver whenthe controller circuit 102 determines that the supervision score isgreater than the threshold indicative of degraded driver-attentiveness,as described above. The controller circuit 102 can pulsate the lights onthe light bar to alert the driver and can use voice and vibrationalalerts in addition to the lighting effects, as described above. Thecontroller circuit 102 can determine when the driver is looking towardthe left and right side of the vehicle and chase 120 the lightpulsations displayed on the light bar to direct the driver's attentiontoward the front of the vehicle, as described above.

EXAMPLES

In the following section, examples are provided.

Example 1

A system, comprising: a controller circuit configured to: receivemonitoring data from a driver-monitor sensor that is configured tomonitor a driver of a vehicle while the vehicle operates in anautonomous-driving mode; determine a score of one or moredriver-supervisory metrics based on the monitoring data, each of thedriver-supervisory metrics being at least partially indicative ofwhether the driver is supervising the operation of the vehicle;determine a supervision score based on the score of the one or moredriver-supervisory metrics, the supervision score being indicative ofwhether the driver is ready to resume control of the vehicle; andindicate a driver-awareness status on a display in a field of view ofthe driver based on the supervision score.

Example 2

The system of the previous example, wherein the driver-monitor sensorincludes one or more of a two-dimensional camera, a three-dimensionalcamera, a steering torque sensor, and a capacitive steering-wheelsensor.

Example 3

The system of any of the previous examples, wherein thedriver-supervisory metrics include one or more of a situationalawareness, a distraction level, an object-in-hand detection, ahands-off-steering-wheel time, a driver presence, a driver-bodyposition, a driver sleep-state, and a driver-drowsiness level.

Example 4

The system of any of the previous examples, wherein the controllercircuit determines the supervision score by determining a maximum valueof any one of the driver-supervisory metrics.

Example 5

The system of any of the previous examples, wherein thesituational-awareness metric is based in part on one or more of adriver's glance direction relative to a forward roadway traveled by thevehicle, a driver's glance duration, and a glance count of the driver.

Example 6

The system of any of the previous examples, wherein the controllercircuit is further configured to determine a first supervision scorethat is indicative of the driver having one or more of a highsituational awareness, the hands-off-steering-wheel time less than afirst threshold, the driver present and in a correct position in adriver's seat, and a high alertness level.

Example 7

The system of any of the previous examples, wherein the controllercircuit is further configured to determine a second supervision scorethat is indicative of the driver having one or more of a cognitiveoverload and the hands-off-steering-wheel time greater than a secondthreshold.

Example 8

The system of any of the previous examples, wherein the controllercircuit is further configured to determine a third supervision scorethat is indicative of the driver having one or more of a low situationalawareness, the hands-off-steering-wheel time greater than a thirdthreshold, the driver not present or not in a correct position in adriver's seat, and a low alertness level.

Example 9

The system of any of the previous examples, wherein the displaycomprises a light bar and the controller circuit is further configuredto change a color of light displayed based on the supervision score.

Example 10

The system of any of the previous examples, wherein the controllercircuit is further configured to pulsate light displayed by the lightbar when the supervision score exceeds a first threshold.

Example 11

The system of any of the previous examples, wherein the controllercircuit is further configured to increase a frequency of lightpulsations when the supervision score exceeds the first threshold for atime exceeding a second threshold.

Example 12

The system of any of the previous examples, wherein the controllercircuit is further configured to determine a gaze direction of thedriver and chase light pulsations based on the gaze direction, therebydirecting a driver's gaze toward a front of the vehicle.

Example 13

The system of any of the previous examples, wherein the light barextends along a length of a dashboard of the vehicle.

Example 14

The system of any of the previous examples, wherein the light barfurther extends along the length of a left-side door and a right-sidedoor of the vehicle.

Example 15

A method, comprising: receiving, with a controller circuit, monitoringdata from a driver-monitor sensor configured to monitor a driver of avehicle; determining, with the controller circuit, a score of one ormore driver-supervisory metrics based on the monitoring data;determining, with the controller circuit, a supervision score based onthe score of the one or more driver-supervisory metrics; and indicating,with the controller circuit, a driver-awareness status on a display in afield of view of the driver based on the supervision score.

Example 16

The method of the previous example, including determining thesupervision score by determining a maximum value of any one of thedriver-supervisory metrics.

Example 17

The method of any of the previous examples, wherein thedriver-supervisory metrics include one or more of a situationalawareness, a distraction level, an object-in-hand detection, ahands-off-steering-wheel time, a driver presence, a driver-bodyposition, a driver sleep-state, and a driver-drowsiness level.

Example 18

The method of any of the previous examples, including determining thesupervision score by determining a first supervision score that isindicative of the driver having one or more of a high situationalawareness, the hands-off-steering-wheel time less than a firstthreshold, the driver present and in a correct position in a driver'sseat, and a high alertness level.

Example 19

The method of any of the previous examples, including determining thesupervision score by determining a second supervision score that isindicative of the driver having one or more of a cognitive overload andthe hands-off-steering-wheel time greater than a second threshold.

Example 20

The method of any of the previous examples, including determining thesupervision score by determining a third supervision score that isindicative of the driver having one or more of a low situationalawareness, the hands-off-steering-wheel time greater than a thirdthreshold, the driver not present or not in a correct position in adriver's seat, and a low alertness level.

CONCLUSION

While various embodiments of the disclosure are described in theforegoing description and shown in the drawings, it is to be understoodthat this disclosure is not limited thereto but may be variouslyembodied to practice within the scope of the following claims. From theforegoing description, it will be apparent that various changes may bemade without departing from the spirit and scope of the disclosure asdefined by the following claims.

The use of “or” and grammatically related terms indicates non-exclusivealternatives without limitation unless the context clearly dictatesotherwise. As used herein, a phrase referring to “at least one of” alist of items refers to any combination of those items, including singlemembers. As an example, “at least one of: a, b, or c” is intended tocover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination withmultiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b,a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b,and c).

What is claimed is:
 1. A system, comprising: a controller circuitconfigured to: receive monitoring data from a driver-monitor sensor thatis configured to monitor a driver of a vehicle while the vehicleoperates in an autonomous-driving mode; determine a score of one or moredriver-supervisory metrics based on the monitoring data, each of thedriver-supervisory metrics being at least partially indicative ofwhether the driver is supervising the operation of the vehicle;determine a supervision score based on the score of the one or moredriver-supervisory metrics, the supervision score being indicative ofwhether the driver is ready to resume control of the vehicle; andindicate a driver-awareness status on a display in a field of view ofthe driver based on the supervision score.
 2. The system of claim 1,wherein the driver-monitor sensor includes one or more of atwo-dimensional camera, a three-dimensional camera, a steering torquesensor, and a capacitive steering-wheel sensor.
 3. The system of claim1, wherein the driver-supervisory metrics include one or more of asituational awareness, a distraction level, an object-in-hand detection,a hands-off-steering-wheel time, a driver presence, a driver-bodyposition, a driver sleep-state, and a driver-drowsiness level.
 4. Thesystem of claim 3, wherein the controller circuit determines thesupervision score by determining a maximum value of any one of thedriver-supervisory metrics.
 5. The system of claim 3, wherein the one ormore driver-supervisory metrics are based in part on one or more of adriver's glance direction relative to a forward roadway traveled by thevehicle, a driver's glance duration, and a glance count of the driver.6. The system of claim 3, wherein the controller circuit is furtherconfigured to determine a first supervision score that is indicative ofthe driver having one or more of a high situational awareness, thehands-off-steering-wheel time less than a first threshold, the driverpresent and in a correct position in a driver's seat, and a highalertness level.
 7. The system of claim 3, wherein the controllercircuit is further configured to determine a second supervision scorethat is indicative of the driver having one or more of a cognitiveoverload and the hands-off-steering-wheel time greater than a secondthreshold.
 8. The system of claim 3, wherein the controller circuit isfurther configured to determine a third supervision score that isindicative of the driver having one or more of a low situationalawareness, the hands-off-steering-wheel time greater than a thirdthreshold, the driver not present or not in a correct position in adriver's seat, and a low alertness level.
 9. The system of claim 1,wherein the display comprises a light bar and the controller circuit isfurther configured to change a color of light displayed based on thesupervision score.
 10. The system of claim 9, wherein the controllercircuit is further configured to pulsate light displayed by the lightbar when the supervision score exceeds a first threshold.
 11. The systemof claim 10, wherein the controller circuit is further configured toincrease a frequency of light pulsations when the supervision scoreexceeds the first threshold for a time exceeding a second threshold. 12.The system of claim 10, wherein the controller circuit is furtherconfigured to determine a gaze direction of the driver and chase lightpulsations based on the gaze direction, thereby directing a driver'sgaze toward a front of the vehicle.
 13. The system of claim 9, whereinthe light bar extends along a length of a dashboard of the vehicle. 14.The system of claim 13, wherein the light bar further extends along thelength of a left-side door and a right-side door of the vehicle.
 15. Amethod, comprising: receiving, with a controller circuit, monitoringdata from a driver-monitor sensor configured to monitor a driver of avehicle while the vehicle operates in an autonomous-driving mode;determining, with the controller circuit, a score of one or moredriver-supervisory metrics based on the monitoring data; determining,with the controller circuit, a supervision score based on the score ofthe one or more driver-supervisory metrics; and indicating, with thecontroller circuit, a driver-awareness status on a display in a field ofview of the driver based on the supervision score.
 16. The method ofclaim 15, further including determining the supervision score bydetermining a maximum value of any one of the driver-supervisorymetrics.
 17. The method of claim 15, wherein the driver-supervisorymetrics include one or more of a situational awareness, a distractionlevel, an object-in-hand detection, a hands-off-steering-wheel time, adriver presence, a driver-body position, a driver sleep-state, and adriver-drowsiness level.
 18. The method of claim 17, further includingdetermining the supervision score by determining a first supervisionscore that is indicative of the driver having one or more of a highsituational awareness, the hands-off-steering-wheel time less than afirst threshold, the driver present and in a correct position in adriver's seat, and a high alertness level.
 19. The method of claim 17,further including determining the supervision score by determining asecond supervision score that is indicative of the driver having one ormore of a cognitive overload and the hands-off-steering-wheel timegreater than a second threshold.
 20. The method of claim 17, furtherincluding determining the supervision score by determining a thirdsupervision score that is indicative of the driver having one or more ofa low situational awareness, the hands-off-steering-wheel time greaterthan a third threshold, the driver not present or not in a correctposition in a driver's seat, and a low alertness level.